load_hillstrom_email_marketing¶
Loading the Hillstrom Email Marketing dataset from the local file.
Data description¶
This dataset contains 64,000 customers who last purchased within twelve months. The customers were involved in an e-mail test.
- 1/3 were randomly chosen to receive an e-mail campaign featuring Mens merchandise.
- 1/3 were randomly chosen to receive an e-mail campaign featuring Womens merchandise.
- 1/3 were randomly chosen to not receive an e-mail campaign.
During a period of two weeks following the e-mail campaign, results were tracked. Your job is to tell the world if the Mens or Womens e-mail campaign was successful.
| Features | 8 |
| Treatment | 3 |
| Samples total | 64,000 |
| Average spend rate | 1.05091 |
| Average visit rate | 0.14678 |
| Average conversion rate | 0.00903 |
More information about dataset you can find in the official paper.
| Parameters: | data_home: str, default=None
Specify another download and cache folder for the dataset.
By default the dataset will be stored in the data folder in the same folder.
load_raw_data: bool, default=False
The loading of raw or preprocessed data?
download_if_missing: bool, default=True
Download the dataset if it is not downloaded.
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| Returns: | dataset: dict
Dictionary object with the following attributes:
dataset.description : str
Description of the Hillstrom email marketing dataset.
dataset.data: numpy ndarray of shape (64000, 8)
Each row corresponding to the 8 feature values in order.
dataset.feature_names: list, size 8
List of feature names.
dataset.treatment: numpy ndarray, shape (64000,)
Each value corresponds to the treatment.
dataset.target: numpy array of shape (64000,)
Each value corresponds to one of the outcomes. By default, it’s spend outcome (look at target_spend below).
dataset.target_spend: numpy array of shape (64000,)
Each value corresponds to how much customers spent during a two-week outcome period.
dataset.target_visit: numpy array of shape (64000,)
Each value corresponds to whether people visited the site during a two-week outcome period.
dataset.target_conversion: numpy array of shape (64000,)
Each value corresponds to whether they purchased at the site (“conversion”) during a two-week outcome period.
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Examples¶
from pyuplift.datasets import load_hillstrom_email_marketing
df = load_hillstrom_email_marketing()
print(df)